DZone
Thanks for visiting DZone today,
Edit Profile
  • Manage Email Subscriptions
  • How to Post to DZone
  • Article Submission Guidelines
Sign Out View Profile
  • Post an Article
  • Manage My Drafts
Over 2 million developers have joined DZone.
Log In / Join
Refcards Trend Reports Events Over 2 million developers have joined DZone. Join Today! Thanks for visiting DZone today,
Edit Profile Manage Email Subscriptions Moderation Admin Console How to Post to DZone Article Submission Guidelines
View Profile
Sign Out
Refcards
Trend Reports
Events
Zones
Culture and Methodologies Agile Career Development Methodologies Team Management
Data Engineering AI/ML Big Data Data Databases IoT
Software Design and Architecture Cloud Architecture Containers Integration Microservices Performance Security
Coding Frameworks Java JavaScript Languages Tools
Testing, Deployment, and Maintenance Deployment DevOps and CI/CD Maintenance Monitoring and Observability Testing, Tools, and Frameworks
Culture and Methodologies
Agile Career Development Methodologies Team Management
Data Engineering
AI/ML Big Data Data Databases IoT
Software Design and Architecture
Cloud Architecture Containers Integration Microservices Performance Security
Coding
Frameworks Java JavaScript Languages Tools
Testing, Deployment, and Maintenance
Deployment DevOps and CI/CD Maintenance Monitoring and Observability Testing, Tools, and Frameworks
  1. DZone
  2. Data Engineering
  3. Databases
  4. JOIN Faster With Couchbase Index JOINs

JOIN Faster With Couchbase Index JOINs

Take a look at Couchbase's index JOINs — a way to perform JOINs quickly and without parents having references to their children documents.

Keshav Murthy user avatar by
Keshav Murthy
CORE ·
Aug. 03, 16 · Tutorial
Like (10)
Save
Tweet
Share
17.54K Views

Join the DZone community and get the full member experience.

Join For Free

Good features in a query language help you to optimize the data model, save space, and increase performance.

Normally, you'd have a child table pointing to its parent. For example, orders have the document key of the customer. So, starting with orders, you join customers to have the fully joined document, which can be processed further.  

Image title


To get the list of orders by zip code, you write the following query:


SELECT c.C_ZIP, COUNT(o.O_ID)
FROM ORDERS AS o LEFT OUTER JOIN CUSTOMER AS c
    ON KEYS o.O_CUSTOMER_KEY
GROUP BY c.C_ZIP
ORDER BY COUNT(1) desc;


This works like a charm. Let's look at the query plan.

We use the primary index on ORDERS to do the full scan. For each document there, try to find the matching CUSTOMER document by using the ORDERS.O_CUSTOMER_KEY as the document key. After the JOIN, grouping, aggregation, and sorting follows.


[
  {
    "plan": {
      "#operator": "Sequence",
      "~children": [
        {
          "#operator": "Sequence",
          "~children": [
            {
              "#operator": "PrimaryScan",
              "index": "#primary",
              "keyspace": "ORDERS",
              "namespace": "default",
              "using": "gsi"
            },
            {
              "#operator": "Parallel",
              "~child": {
                "#operator": "Sequence",
                "~children": [
                  {
                    "#operator": "Fetch",
                    "as": "o",
                    "keyspace": "ORDERS",
                    "namespace": "default"
                  },
                  {
                    "#operator": "Join",
                    "as": "c",
                    "keyspace": "CUSTOMER",
                    "namespace": "default",
                    "on_keys": "(`o`.`O_CUSTOMER_KEY`)",
                    "outer": true
                  },
                  {
                    "#operator": "InitialGroup",
                    "aggregates": [
                      "count((`o`.`O_ID`))",
                      "count(1)"
                    ],
                    "group_keys": [
                      "(`c`.`C_ZIP`)"
                    ]
                  }
                ]
              }
            },
            {
              "#operator": "IntermediateGroup",
              "aggregates": [
                "count((`o`.`O_ID`))",
                "count(1)"
              ],
              "group_keys": [
                "(`c`.`C_ZIP`)"
              ]
            },
            {
              "#operator": "FinalGroup",
              "aggregates": [
                "count((`o`.`O_ID`))",
                "count(1)"
              ],
              "group_keys": [
                "(`c`.`C_ZIP`)"
              ]
            },
            {
              "#operator": "Parallel",
              "~child": {
                "#operator": "Sequence",
                "~children": [
                  {
                    "#operator": "InitialProject",
                    "result_terms": [
                      {
                        "expr": "(`c`.`C_ZIP`)"
                      },
                      {
                        "expr": "count((`o`.`O_ID`))"
                      }
                    ]
                  }
                ]
              }
            }
          ]
        },
        {
          "#operator": "Order",
          "sort_terms": [
            {
              "desc": true,
              "expr": "count(1)"
            }
          ]
        },
        {
          "#operator": "FinalProject"
        }
      ]
    },
    "text": "SELECT c.C_ZIP, COUNT(o.O_ID)\nFROM ORDERS AS o LEFT OUTER JOIN CUSTOMER AS c\n         ON KEYS o.O_CUSTOMER_KEY\nGROUP BY c.C_ZIP\nORDER BY COUNT(1) desc;"
  }
]


But what if you're interested California (CA) residents only? Simply add a predicate on the C_STATE field.


SELECT c.C_ZIP, COUNT(o.O_ID)
FROM ORDERS AS o LEFT OUTER JOIN CUSTOMER AS c
    ON KEYS o.O_CUSTOMER_KEY
WHERE c.C_STATE = "CA"
GROUP BY c.C_ZIP
ORDER BY COUNT(1) desc;


This works, except we end up scanning all of the orders, whether the orders belong to California or not. Only after the JOIN operation do we apply the C_STATE = "CA" filter.  In a large data set, this has negative performance impact.  What if we could improve the performance by limiting the amount of data accessed on the ORDERS bucket.

This is exactly what the index JOINs feature will help you do. The alternate query is below.


SELECT c.C_ZIP, COUNT(o.O_ID)
FROM CUSTOMER AS c LEFT OUTER JOIN ORDERS AS o
         ON KEY o.O_CUSTOMER_KEY FOR c
WHERE c.C_STATE = "CA"
GROUP BY c.C_ZIP
ORDER BY COUNT(1) desc;


You do need an index on ORDERS.O_CUSTOMER_KEY.

To further improve the performance, you can create the index on CUSTOMER.C_STATE.


CREATE INDEX idx_okey ON ORDERS(O_CUSTOMER_KEY);


With these indexes, you get a plan like the following:


CREATE INDEX idx_cstate ON CUSTOMER(C_STATE);


Let's examine the explanation. We use two indexes idx_cstate, which scans the CUSTOMER with the predicate (C_STATE = "CA"), and then idx_okey, which helps to find the matching document in ORDERS.


[
  {
    "plan": {
      "#operator": "Sequence",
      "~children": [
        {
          "#operator": "Sequence",
          "~children": [
            {
              "#operator": "IndexScan",
              "index": "idx_cstate",
              "index_id": "a3a663ec9928d888",
              "keyspace": "CUSTOMER",
              "namespace": "default",
              "spans": [
                {
                  "Range": {
                    "High": [
                      "\"CA\""
                    ],
                    "Inclusion": 3,
                    "Low": [
                      "\"CA\""
                    ]
                  }
                }
              ],
              "using": "gsi"
            },
            {
              "#operator": "Parallel",
              "~child": {
                "#operator": "Sequence",
                "~children": [
                  {
                    "#operator": "Fetch",
                    "as": "c",
                    "keyspace": "CUSTOMER",
                    "namespace": "default"
                  },
                  {
                    "#operator": "IndexJoin",
                    "as": "o",
                    "for": "c",
                    "keyspace": "ORDERS",
                    "namespace": "default",
                    "on_key": "(`o`.`O_CUSTOMER_KEY`)",
                    "outer": true,
                    "scan": {
                      "index": "idx_okey",
                      "index_id": "271ea96d9390e10d",
                      "using": "gsi"
                    }
                  },
                  {
                    "#operator": "Filter",
                    "condition": "((`c`.`C_STATE`) = \"CA\")"
                  },
                  {
                    "#operator": "InitialGroup",
                    "aggregates": [
                      "count((`o`.`O_ID`))",
                      "count(1)"
                    ],
                    "group_keys": [
                      "(`c`.`C_ZIP`)"
                    ]
                  }
                ]
              }
            },
            {
              "#operator": "IntermediateGroup",
              "aggregates": [
                "count((`o`.`O_ID`))",
                "count(1)"
              ],
              "group_keys": [
                "(`c`.`C_ZIP`)"
              ]
            },
            {
              "#operator": "FinalGroup",
              "aggregates": [
                "count((`o`.`O_ID`))",
                "count(1)"
              ],
              "group_keys": [
                "(`c`.`C_ZIP`)"
              ]
            },
            {
              "#operator": "Parallel",
              "~child": {
                "#operator": "Sequence",
                "~children": [
                  {
                    "#operator": "InitialProject",
                    "result_terms": [
                      {
                        "expr": "(`c`.`C_ZIP`)"
                      },
                      {
                        "expr": "count((`o`.`O_ID`))"
                      }
                    ]
                  }
                ]
              }
            }
          ]
        },
        {
          "#operator": "Order",
          "sort_terms": [
            {
              "desc": true,
              "expr": "count(1)"
            }
          ]
        },
        {
          "#operator": "FinalProject"
        }
      ]
    },
    "text": "SELECT c.C_ZIP, COUNT(o.O_ID)\nFROM CUSTOMER AS c LEFT OUTER JOIN ORDERS AS o\n         ON KEY o.O_CUSTOMER_KEY FOR c\nWHERE c.C_STATE = \"CA\"\nGROUP BY c.C_ZIP\nORDER BY COUNT(1) desc;"
  }
]


So, how does this plan execute? Let's look at the visual version of this.

Image title


We first initiate the index scan on CUSTOMER.idx_state and pushdown the filter (c.C_STATE = “CA”). Index scan returns a list of qualified customers. In this case, the CUSTOMER document key is "1.10.1938." We retrieve the CUSTOMER document, then initiate the index scan on ORDERS.idx_okey with the predicate on the CUSTOMER document key (ORDERS.O_CUSTOMER_KEY = "1.10.1938").  That scan returns the document key of the ORDERS, "1.10.143."

Comparing plan 1 with plan 2, the plan to uses two indices to minimize the amount of data to retrieve and process. It, therefore, performs faster.

The index JOIN feature is composable. You can use index JOIN as part of any of JOIN statements to help you navigate through your data model. For example:


SELECT c.C_ZIP, COUNT(o.O_ID), COUNT(ol.OL_ORDER_ITEMS)
FROM CUSTOMER AS c LEFT OUTER JOIN ORDERS AS o
         ON KEY o.O_CUSTOMER_KEY FOR c
     INNER JOIN ORDER_LINE ol
      ON KEYS o.O_OL_ORDER_KEY
WHERE c.C_STATE = "CA"
GROUP BY c.C_ZIP
ORDER BY COUNT(1) desc;


Try it yourself. I've given examples you can try out yourself on Couchbase 4.5 using the beer-sample dataset shipped with it.  Check out the slides here.

Summary

Index joins help you to join tables from parent-to-child even when the parent document does not have a reference to its children documents. You can use this feature with INNER JOINS and LEFT OUTER JOINS. This feature is composable. You can have a multi-join statement, with only some of them exploiting index joins. 

Database Joins (concurrency library) Document

Opinions expressed by DZone contributors are their own.

Popular on DZone

  • How Do the Docker Client and Docker Servers Work?
  • Kubernetes vs Docker: Differences Explained
  • How to Secure Your CI/CD Pipeline
  • Distributed SQL: An Alternative to Database Sharding

Comments

Partner Resources

X

ABOUT US

  • About DZone
  • Send feedback
  • Careers
  • Sitemap

ADVERTISE

  • Advertise with DZone

CONTRIBUTE ON DZONE

  • Article Submission Guidelines
  • Become a Contributor
  • Visit the Writers' Zone

LEGAL

  • Terms of Service
  • Privacy Policy

CONTACT US

  • 600 Park Offices Drive
  • Suite 300
  • Durham, NC 27709
  • support@dzone.com
  • +1 (919) 678-0300

Let's be friends: